Tootfinder

Tubi says it crossed 100M monthly users in May and accounted for 1B hours of total TV viewership time; Nielsen: Tubi accounted for 2.2% of total TV viewership (Kayla Cobb/The Wrap)
https://www.thewrap.com/tubi-users-100-million-monthly-nielsen/

Tubi Surpasses 100 Million Monthly Users for the First Time
Tubi surpassed 100 million monthly hours and accounted for 1 billion hours of total TV viewing in May, a first according to Nielsen.

Nielsen: in May, Americans watched more TV on streaming than on cable and broadcast networks combined, the first time that has happened over a full month (John Koblin/New York Times)
https://www.nytimes.com/2025/06/17/business/media/streaming…

It’s Official: Streaming Is Now the King of TV
In May, more Americans watched television on streaming than on cable and network television combined, Nielsen said. It is the first time that has happened over a full month.

Nielsen numbers of $n$-valued maps on infra-solvmanifolds
Karel Dekimpe, Lore De Weerdt
https://arxiv.org/abs/2506.13438 https://arxi…

Nielsen numbers of $n$-valued maps on infra-solvmanifolds
We derive a formula for the Nielsen number $N(f)$ for every $n$-valued self-map $f$ of an infra-solvmanifold. To do this, we express $N(f)$ in terms of Nielsen coincidence numbers of single-valued maps on solvmanifolds, and derive a formula for Nielsen coincidence numbers in that setting.

Condition Monitoring with Machine Learning: A Data-Driven Framework for Quantifying Wind Turbine Energy Loss
Emil Marcus Buchberg, Kent Vugs Nielsen
https://arxiv.org/abs/2506.13012

Condition Monitoring with Machine Learning: A Data-Driven Framework for Quantifying Wind Turbine Energy Loss
Wind energy significantly contributes to the global shift towards renewable energy, yet operational challenges, such as Leading-Edge Erosion on wind turbine blades, notably reduce energy output. This study introduces an advanced, scalable machine learning framework for condition monitoring of wind turbines, specifically targeting improved detection of anomalies using Supervisory Control and Data Acquisition data. The framework effectively isolates normal turbine behavior through rigorous prepro…

🇺🇦 #NowPlaying on KEXP's #Roadhouse
Harry Nilsson:
🎵 Me and My Arrow
#HarryNilsson
https://wetbridgerecords.bandcamp.com/album/ty-segall-loch-lomond-gotta-get-up-me-my-arrow-harry-nilsson-cover-7inch
https://open.spotify.com/track/5KjRITicSHJM88w3U9LwLM
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So uh, no 6 or 7 on my block for a bit.

Nielsen: in May, Americans watched more TV on streaming than on cable and broadcast networks combined, the first time that has happened over a full month (John Koblin/New York Times)
https://www.nytimes.com/2025/06/17/business/media/streaming…

It’s Official: Streaming Is Now the King of TV
In May, more Americans watched television on streaming than on cable and network television combined, Nielsen said. It is the first time that has happened over a full month.

A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning
Asbj{\o}rn Munk, Stefano Cerri, Jakob Ambsdorf, Julia Machnio, Sebastian N{\o}rgaard Llambias, Vardan Nersesjan, Christian Hedeager Krag, Peirong Liu, Pablo Rocamora Garc\'ia, Mostafa Mehdipour Ghazi, Mikael Boesen, Michael Eriksen Benros, Juan Eugenio Iglesias, Mads Nielsen

A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning
We present FOMO60K, a large-scale, heterogeneous dataset of 60,529 brain Magnetic Resonance Imaging (MRI) scans from 13,900 sessions and 11,187 subjects, aggregated from 16 publicly available sources. The dataset includes both clinical- and research-grade images, multiple MRI sequences, and a wide range of anatomical and pathological variability, including scans with large brain anomalies. Minimal preprocessing was applied to preserve the original image characteristics while reducing barriers t…

Using Model Predictive Control To Reduce Traffic Emissions on Urban Freeways
Alexander Hammerl, Ravi Seshadri, Thomas Kj{\ae}r Rasmussen, Otto Anker Nielsen
https://arxiv.org/abs/2506.13393

Using Model Predictive Control To Reduce Traffic Emissions on Urban Freeways
Urban traffic congestion significantly impacts regional air quality and contributes substantially to pollutant emissions. Suburban freeway corridors are a major source of traffic-related emissions, particularly nitrogen oxides (NOx) and carbon dioxide (CO2). This paper proposes a Model Predictive Control (MPC) framework aimed at emission reduction on peripheral freeway corridors. Emission rates on freeways exhibit high sensitivity to speed fluctuations and congestion recovery processes. To addr…

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